PUXplore Multidisciplinary Journal of Engineering
PUXplore: Multidisciplinary Journal of Engineering is a peer-reviewed, open-access academic journal dedicated to the advancement of engineering research across diverse disciplines. Published by Parul University, the journal serves as a dynamic platform for scholars, researchers, industry professionals, and academicians to disseminate innovative findings, practical insights, and theoretical advancements that address real-world engineering challenges. Scope and Aim: The journal welcomes original research articles, review papers, technical notes, and case studies from all major branches of engineering, including but not limited to: Artificial Intelligence and Data Science Computer Science and Engineering Electrical, Electronics, and Communication Engineering Mechanical and Automobile Engineering Civil and Structural Engineering Chemical and Environmental Engineering Mechatronics, Robotics, and Embedded Systems Biomedical and Biotechnology Engineering Internet of Things (IoT), Cybersecurity, and Smart Systems ParulXplore aims to foster interdisciplinary collaboration, bridge academia and industry, and contribute to national and global engineering knowledge ecosystems. Emphasis is given to research that integrates innovation, sustainability, digital transformation, and technological excellence. Frequency: Monthly Article Format: Double-blind peer review | APA/IEEE referencing | Plagiarism check mandatory Language of Publication: English Mode of Access: Online (Open Access) Key Features: Multidisciplinary Focus with Engineering Core Rigorous Peer Review Process Fast and Transparent Publication Workflow No Article Processing Charges (APCs) [Subject to editorial policy] Indexed in National and International Databases [In progress] Editorial Board: The journal is supported by a distinguished editorial board comprising leading academicians, researchers, and industry experts from India and abroad, ensuring the highest standards of academic integrity and publication ethics. Target Audience: Engineering Faculty and Research Scholars Industry Practitioners and R&D Professionals Postgraduate and Doctoral Students Policy Makers and Innovators in Technology Call for Papers: ParulXplore invites high-quality submissions from researchers and practitioners for upcoming issues. All papers must be original, unpublished, and not under consideration elsewhere.
All articles published in PUXplore: Multidisciplinary Journal of Engineering are licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. Under this license, anyone may read, download, copy, distribute, and share the work for non-commercial purposes, provided that appropriate credit is given to the author(s), the journal, and a link to the license is included. No adaptations, derivatives, or modifications of the work are permitted without prior written permission from the copyright holder. Authors retain copyright and grant the journal the right of first publication under this license.
STRENGTHENING THE SECURITY OF TWO-FACTOR AUTHENTICATION USING CRYPTOGRAPHIC AND DEVICE-BASED ENHANCEMENTS
Let’s face it, the way we live has completely changed with the rise of all things digital. Now, keeping user accounts safe isn’t just a nice-to-have—it’s absolutely vital. To secure accounts, Two-Factor Authentication (2FA) is the method most people use nowadays to have an extra layer of security beyond password-based systems. Unfortunately, as a result of sophisticated attackers' usage of phishing, SIM swapping, replay attacks, and session hijacking, conventional 2FA mechanisms have been found to mostly be vulnerable. This study introduces a new 2FA framework, which combines secure cryptographic methods, device-based verification, and an improved OTP generation mechanism, to make the system more resistant to such kinds of threats. The system being proposed will have a way for the user to generate an OTP on the client side. In addition, it supports the usage of secure key exchange protocols and also the encrypted communication between client and server so as to avoid the capturing and replaying of authentication credentials by a third party. Resistance to common cyberattacks is the major focus of the prototype system subjected to testing under simulated attack scenarios. Quantitative metrics such as authentication time, error rate, and attack success probability have been analysed. The anticipated result is an authentication model that is more robust and less vulnerable to attacks and hence offers increased security without compromising on usability. The present research is a field-mover in security measures around authentication and, subsequently, in trust and safety on digital ​‍​‌‍​‍‌​‍​‌‍​‍‌platforms.
Copyright (c) 2026 Isha Patel, Gordhan Jethava (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
BIBLIOMETRIC ANALYSIS SUPPORTED REVIEW ON WEB 3.0 BASED DATA ANALYTICS FOR IIOT ENABLED SYSTEM
Web 3.0 technology is currently attracting more attention regarding its critical application in the use of next-generation data analytics in the IIoT industry. Web 3.0 technology acts as a basis for the development of a distributed smart architecture in the IIoT industry. Various research studies have proved that the use of data analytics can generate a great improvement in the context of efficiency and decision-making in the IIoT industry. Scalability issues, interoperability issues, real-time data analytics, and issues of privacy are considered to be the challenges in the current data analytics systems in the IIoT industry. The review article will present a current understanding on the basis of the latest research studies on the application of Web 3.0 technology in the context of data analytics in the IIoT industry. Through systematic and bibliometric analysis, the paper identifies the research trends, thematic development, and research gaps in the available literature on data analytics in the Web 3.0-IIoT domain.
Copyright (c) 2026 Nehaben Patel, Minal Shukla, Pooja Sapra, Amit Sata, Mrudul Jani, Pravin Vadhel (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
SECURE IOT-BASED AUTOMATIC GATE SYSTEM WITH DECENTRALIZED AUTHENTICATION AND BIBLIOMETRIC ANALYSIS
The use of IoT-based automatic gate systems is on the rise in smart cities, healthcare, and transportation sectors; however, centralized authentication systems create single points of failure, scalability issues, and security risks. In this paper, a secure automatic gate system using decentralized blockchain-enabled authentication is proposed. Smart contracts are utilized to implement immutable access control without the need for a central authority. A machine learning-based anomaly recognition framework is incorporated to identify and prevent malicious or anomalous access attempts in real-time. Edge and fog computing concepts are leveraged to minimize latency and improve energy efficiency. In addition, a bibliometric study of peer-reviewed articles (2015-2025) on blockchain-enabled verification is performed using Scopus and Web of Science® datasets. The bibliometric study is carried out in the RStudio® environment using the Bibliometrics package, exploring publication patterns, citation effects, collaboration patterns, and topic evolution. The findings show that research has expanded rapidly since 2018 with a focus on permissioned and hybrid blockchain models. Research gaps in energy efficiency, interoperability, and large-scale deployment of secure IoT access control systems are identified.
Copyright (c) 2026 Alok Pandit, Minal Shukla, Pooja Sapra, Amit Sata, Payal Singh, Pravin Vadhel (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A SYSTEMATIC REVIEW OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR COMPUTER VISION APPLICATIONS
Convolutional Neural Networks (CNNs) have contributed to significant progress in computer vision activities like image classification, object detection, segmentation, and autonomous navigation. This paper is a systematic review of CNN architectures that were posted between 2012 and 2025, examining their development, uses, effectiveness, restrictions and future tendencies. The results also emphasize the move to efficient, scalable, and deployable frameworks, where the emphasis is on lightweight and hybrid frameworks in real-time and edge applications, which can guide the development of intelligent vision systems in the future.
Copyright (c) 2026 Maitrakkumar Manojkumar Patel, jaipraksh Dwivedi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
INTEGRATING BLOCKCHAIN FOR METAVERSE OF MANUFACTURING SYSTEM: BIBLIOMETRIC ANALYSIS, AND FRAMEWORK
The combination of blockchain and Metaverse is progressing toward future beyond its current paradigm, promising to be a game-changing technology in area of Next-Generation Manufacturing Industry 4.0 and Smart Factory concepts. world of Industry 4.0, or Industrial part of Metaverse, defined by concept of virtual, immersive and collaborative environments in which digital twins, IoT devices other Cyber-Physical-System objects communicate at the speed of light; however, trust and security remain major barriers to mainstream adoption. Current research examines integration of blockchain its junction with concept and implementation of Metaverse through lens of bibliometrics and literature survey. Based on Scopus and Web of Science data, the bibliometric trend evaluation revealed a significant growth in blockchain-based Metaverse application research, albeit with few manufacturing applications. The review highlighted important uses of decentralized identification, secure data exchange, digital twin synchronization, and smart contract-based automation. Despite the advances achieved in development of Metaverse technology, a few major difficulties remain in the sector. As deficiencies indicated above exist, study provides a framework combines potential of blockchain, digital twins and decentralized governance concepts in industrial Metaverse setting, as demonstrated by results. Article bridges gap between theoretical discoveries real-world implementation of blockchain-based industrial Metaverse platforms.
Copyright (c) 2026 Utkarsh kumar, Pravin Vadhel, Pragyan Nanda, Amit Sata, Minal Shukla, Chirag R. Trivedi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
MACHINE LEARNING-BASED DATABASE ANOMALY DETECTION: A COMPREHENSIVE REVIEW
Database management systems are critical components of modern enterprise infrastructures and are increasingly targeted by sophisticated cyberattacks. Traditional rule-based intrusion detection systems are limited in identifying evolving and zero-day threats due to their static nature. Machine learning (ML) techniques provide adaptive, datadriven solutions for modeling normal database behavior and detecting anomalous activities. This paper presents a concise review of machine learning-based approaches for database anomaly detection, categorizing existing methods into clustering, classification, ensemble learning, and deep learning frameworks. A comparative analysis highlights the strengths and limitations of each category in terms of detection accuracy, recall, scalability, and interpretability. Ensemble methods demonstrate improved robustness and performance in imbalanced datasets, while deep learning models effectively capture complex and temporal behavioral patterns. Key challenges, including class imbalance, scalability constraints, and limited explainability, are discussed, along with emerging research directions such as federated learning and graph-based modeling.
Artificial Intelligence and Machine Learning
Copyright (c) 2026 Harnish Shah, Dhirenkumar Purohit (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
KNOWLEDGE REPRESENTATION OF IIOT ENABLED MANUFACTURING SYSTEM USING ONTOLOGY: BIBLIOMETRIC ANALYSIS AND FRAMEWORK
Knowledge representation has been considered the backbone of artificial intelligence. Ontology methods for knowledge representation have been found to be extremely beneficial for the field of artificial intelligence. The ontology method itself affirms the applicability of the method in the case of the representation of domain knowledge. While considering knowledge representation with the help of the ontology method, a literate survey has to be fully mentioned. This literature survey was mentioned in the paper with help of the “use of ontology models and knowledge graph models”. It recognizes the general methodologies used, i.e., knowledge acquisition, concept extraction, ontology development, ontology integration, and evalution of the ontology. Except for that, the system recognizes some of the areas not properly handled by the research, such as the automation of ontologies, the interoperability of the ontology, the evolution of the ontology, and the future of the research, e.g., the learning of ontologies by AI. Conclusively, the representation of knowledge in conformity with the utilization of the ontologies ensures that semantic interoperation, reasoning, and shareability of the knowledge is carried out in an interoperable mode, a necessity for the development of state-of-the-art knowledge-based applications.
Copyright (c) 2026 Jensi Amrutiya, Tejal Patel, Minal Shukla, Amit Sata (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Algorithmic Evolution of Differential Privacy: A Decade of Theoretical Advances and Practical Implementations
This comprehensive survey presents a systematic examination of the evolution of differential privacy algorithms over the past decade, tracing their journey from theoretical constructs to practically deployable privacy solutions. Through a rigorous methodological framework encompassing taxonomic classification, experimental evaluation, and evolutionary analysis, the study synthesizes developments across key algorithmic paradigms, including privacy definitions, composition theorems, mechanism design, and computational optimizations. The analysis reveals that modern algorithms achieve 40–60% superior utility preservation under equivalent privacy constraints compared to foundational approaches, demonstrating significant maturation of the field. The research establishes a multi-dimensional taxonomy categorizing 45 algorithms across privacy definitions, algorithmic paradigms, and application domains, providing a structured framework for understanding the algorithmic landscape. Experimental results demonstrate that concentrated differential privacy formulations and adaptive mechanisms achieve flatter utility–privacy trade off curves, while domain-specific algorithms outperform general-purpose approaches by 15–40% within their target domains. The study identifies composition efficiency as a critical factor, with advanced frameworks enabling up to 3.2 times more queries under fixed privacy budgets compared to basic composition methods. Furthermore, the analysis reveals substantial computational trade offs, where increased algorithmic sophistication introduces 3–10 times higher processing requirements. The survey concludes by outlining a research agenda that addresses emerging challenges in high-dimensional data, heterogeneous composition, and integration with emerging technologies. Overall, this work serves as both an authoritative reference for established researchers and an accessible entry point for newcomers seeking to understand the current state and future trajectory of the algorithmic foundations of differential privacy.
Copyright (c) 2026 Dr. Sanjay Agal, Ms. Krishna Raulji, Nikunj Bhavsar, Kinjal Gandhi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cyberbullying Detection Using Machine Learning:A Comprehensive Review
The rapid growth of social media platforms and online communication technologies has increased the scale of digital interactions while simultaneously introducing serious challenges related to harmful online behavior such as cyberbullying. Cyberbullying involves the use of digital platforms to harass, threaten, or humiliate individuals, often causing significant psychological and emotional harm. With the massive volume of user-generated content shared across social networking sites, detecting such harmful behavior through manual moderation has become increasingly difficult. As a result, machine learning techniques have emerged as an effective approach for automatically identifying cyberbullying in online environments. This study applies a systematic literature review (SLR) approach to examine research published over the past decade (2015–2025) on cyberbullying detection using machine learning methods. The review analyzes existing studies to understand commonly used algorithms, datasets, feature extraction techniques, and evaluation strategies applied in cyberbullying detection systems. It also highlights the role of natural language processing and deep learning approaches in improving the identification of abusive online content. In addition, the study discusses key challenges associated with cyberbullying detection, including contextual language understanding, sarcasm interpretation, dataset imbalance, and multilingual communication. This comprehensive analysis aims to provide researchers and practitioners with a clearer understanding of current developments and potential future directions in automated cyberbullying detection for safer online environments.
Cyberbullying Detection, Machine Learning, Natural Language Processing, Online Harassment Detection, , Text Classification, Deep Learning, Automated Content Moderation
Copyright (c) 2026 Dr. Ashwini Kumar Jha, Avinash Songa, Vishnu Ulliboyina, Pavan Kumar Chadaram, Rohith Purushottam Naidu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Review of Security, Privacy, and Authentication Mechanisms in Social Media Web Applications
Social media was never supposed to become this complicated. What started as a way to stay connected has turned into one of the most contested spaces in modern life where fake identities thrive, personal data gets harvested in ways users never agreed to, and the systems meant to verify who we are can barely keep up. Researchers have thrown a lot at these problems over the years, and there has been genuine progress. But an uncomfortable pattern keeps repeating itself, solutions that look great on paper tend to wobble once they hit the real world. The privacy paradox still has no clean answer. Detection models still break when moved between platforms. Authentication still trades security for convenience in ways nobody is fully happy with. Until the technical side of this work starts taking human behavior and policy seriously not as afterthoughts but as core design constraints, the same gaps will keep reappearing.
Copyright (c) 2026 Dr. Ashwini Kumar Jha, Akil Khatri, Kavi Kanda, Areeb Haider, Raunak Shah (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Leveraging AI for Enhanced Solar Energy Efficiency and Prediction
As solar photovoltaics (PV) have emerged as the most economical source of electricity globally, improving the efficiency, reliability, and scalability of PV power plants has become increasingly critical. Traditional performance evaluation methods, which rely on isolated and plant-specific assessments, are inadequate for managing the operational complexity of large-scale and rapidly expanding solar infrastructures. Consequently, intelligent and automated approaches are required to enhance system performance and reduce efficiency losses. Artificial intelligence (AI) provides a powerful framework for data-driven optimization of PV modules and plants by leveraging advanced machine learning and deep learning techniques. AI-based models enable accurate solar power generation forecasting through the integration of historical output data, real-time sensor measurements, and meteorological parameters. Additionally, intelligent diagnostics support early fault detection, reliability assessment, and root-cause analysis of performance degradation caused by soiling, shading, thermal stress, inverter faults, and module aging. Explainable AI further enhances decision-making by improving model transparency and trustworthiness in operational environments. By enabling predictive maintenance and adaptive control strategies, AI-driven PV systems significantly improve energy yield, operational resilience, and asset longevity. This study investigates AI-enabled methodologies for performance enhancement, output forecasting, and degradation analysis in photovoltaic power plants, highlighting their essential role in advancing efficient, autonomous, and sustainable solar energy systems for future smart grid applications.
Artificial intelligence, solar, energy, IoT.
Copyright (c) 2026 Dr Syed Ibad Ali Ali (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Machine Learning Architecture Integrating Spatiotemporal Graph Networks with Differentiable Optimization for Adaptive System Management
This research addresses the fundamental challenge of adaptive system management in complex, dynamic environments by developing a novel integrated machine learning framework that bridges the historical divide between sophisticated spatiotemporal modeling and constraint-aware optimization. Traditional approaches typically treat relational reasoning and decision optimization as sequential processes, fundamentally limiting their ability to capture the intricate interdependencies and evolving constraints that characterize real-world systems. In response, we propose a unified architecture that seamlessly integrates dynamic spatiotemporal graph neural networks with differentiable optimization layers, enabling bidirectional information flow and end-to-end learning of both system dynamics and optimal decisions. The framework incorporates three core innovations: a dynamic graph construction module that learns time-varying relational structures from multimodal data, hierarchical spatiotemporal blocks that capture multi-scale temporal patterns while maintaining spatial context, and differentiable optimization mechanisms that incorporate domain constraints directly into the learning process while supporting regime-aware adaptation. Comprehensive experimental evaluation across two distinct application domains—campus infrastructure management and financial portfolio optimization—demonstrates the framework's superior performance and practical utility. In campus infrastructure forecasting, the framework achieves a 16.3% reduction in mean absolute error compared to the strongest baseline, with particular strength during anomalous events and regime transitions where traditional approaches falter. For portfolio optimization, the framework delivers a Sharpe ratio of 1.38 during the out-of-sample period (2017-2022), representing a 23.2% improvement over contemporary deep learning approaches and a 55.1% enhancement over traditional risk parity strategies, while simultaneously reducing maximum drawdown by 41%. Crucially, the framework exhibits genuine predictive adaptation capabilities, proactively adjusting to changing conditions rather than reacting to realized outcomes, as evidenced during market crises where it began risk reduction weeks before market troughs. Ablation studies confirm that each architectural component contributes significantly to overall performance, with the integrated architecture yielding synergistic improvements unavailable to sequential approaches. The framework demonstrates robust generalization across domains, maintaining 90% of domain-specific performance when transferred between fundamentally different applications with moderate fine-tuning. Computational analysis confirms practical deployment feasibility, with inference latency under 200 milliseconds for typical system sizes, while interpretability mechanisms provide actionable insights for domain experts. This research establishes a new paradigm for adaptive system management, offering both immediate practical value for specific applications and a foundational architecture for broader advances in intelligent decision-making for complex systems characterized by relational dependencies, temporal dynamics, and evolving constraints.
integrated machine learning architecture
Spatiotemporal Graph Neural Networks (ST-GNNs)
Constraint Satisfaction in Neural Networks
Copyright (c) 2026 Dr. Sanjay Agal, Ms. Niyati Dhirubhai Odedra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Neuro-Symbolic AI: Combining Symbolic Reasoning with Neural Networks for Explainable Decision-Making in Interdisciplinary Domains
Artificial Intelligence is performing well in solving some problems but is facing major challenges, as it tries to balance between the models' expressiveness and its interpretation. Despite their expertise in pattern recognition from unstructured data, neural networks remain significantly ambiguous 'black boxes.' Conversely, symbolic reasoning systems provide transparent, rule-based decision-making but struggle with data-driven adaptability. We are illustrating a framework for Neuro-Symbolic AI that describes the integration of neural networks with symbolic reasoning, so as to achieve both the powerful learning and explainable inference. We propose a bidirectional hybrid architecture comprising three layers: (1) a neural processing layer for feature extraction from unstructured data; (2) a symbolic reasoning layer encoding domain knowledge through knowledge graphs and rule-based systems; and (3) an integration layer facilitating semantic communication via differentiable programming. Through experiments on Visual Question Answering (achieving 96.4% accuracy), Natural Language Inference (88.7% accuracy), and Robotics Navigation (94.3% success rate), we demonstrate consistent improvements of 5–20% in accuracy and 2–3× gains in explainability over pure neural or symbolic baselines. This integrated framework have the capacity to deliver interpretable, robust, and generalizable AI in certain critical applications in healthcare, finance, and autonomous systems, The paper addresses scalability challenges, discusses ethical implications, and identifies future research directions including automated knowledge extraction, advanced neural-symbolic interfaces, and real-time adaptive reasoning.
Copyright (c) 2026 Dr. Manoj Kumar Mishra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Smart home automation systems, enhanced through artificial intelligence (AI) and Internet of Things (IoT) technologies, are rapidly transforming traditional living spaces into intelligent, adaptive environments. This study examines the integration of machine learning algorithms, interconnected sensors, and predictive analytics to automate household appliance management, optimize energy consumption, and bolster security. The system utilizes real-time data analysis to adjust operations based on user behavior and ambient conditions, delivering greater energy efficiency, convenience, and safety. Experimental results demonstrate a reduction in manual intervention, significant energy savings, and improved responsiveness in appliance control. The research highlights the effectiveness of AI-powered automation in supporting sustainable, user-friendly homes and outlines future directions for deeper personalization, enhanced security, and scalable design in smart home ecosystems.
Artificial Intelligence of Things (AIoT)
Copyright (c) 2026 PAVAN KUMAR RODDA, Vivek Dave, Krishna Reddy Yaramala, Rishwanth Korlimalla, Vinay Kumar Mule (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Deepfake Detection Using ResNeXt-50 and LSTM Neural Networks
Recent advances in deep learning methods have made it possible to create extremely convincing synthetic media, known by the general public as deepfake videos, that carry enormous dangers such as spreading misinformation, political manipulation, financial scams, and individual blackmail. In this research, we present a system based on deep learning to automate the detection of deepfake videos. In the proposed methodology, we adopt the usage of the ResNeXt-50 architecture to learn the effective frame- level features of the video, which pass through a Long Short-Term Memory (LSTM) network that recognizes the relationships over the whole video. To enhance the generalizability of the model, we adopt a sequential setup with ReLU activation and dropout regularization. To evaluate the model’s effectiveness, we utilize benchmark datasets of deepfakes, and our results show that the model of ResNeXt-50 + LSTM is highly effective in distinguishing between real and manipulated videos. The results of this research provide a significant demonstration of the benefit of fusing multi- dimensional data (i.e., spatial and temporal data) to construct a reliable model to identify deepfakes.
Copyright (c) 2026 PAVAN KUMAR RODDA (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Data Analytics Framework for Measuring the Efficacy of Project Based Learning on SDG Focused FinTech Projects in Indian Universities
This study addresses the critical challenge of evaluating the efficacy of innovative pedagogical approaches in technology education by developing and validating a comprehensive data analytics framework for assessing Project-Based Learning in Sustainable Development Goals-focused FinTech education within Indian universities. The research emerges from the identified gap in empirical measurement tools capable of capturing the multi-dimensional outcomes of experiential learning in complex, interdisciplinary domains. Employing a Design Science Research methodology, the study conceptualizes and implements a novel framework that integrates cognitive, behavioral, and affective dimensions of learning through multi-modal data collection and advanced analytical techniques. The experimental implementation involved a longitudinal case study with 42 participants in a Master's level Sustainable FinTech Solutions course over a 16-week semester. Results demonstrate significant improvements across all dimensions: cognitive assessments revealed substantial knowledge gains with effect sizes of 1.74, behavioral analysis showed progressive enhancement of collaboration patterns and problem-solving capabilities, and affective measures indicated positive transformations in engagement, motivation, and sustainability awareness. Correlation analysis revealed strong interrelationships between dimensions, particularly between behavioral and affective factors (r = 0.73), highlighting their synergistic role in learning processes. The framework successfully provided real-time, actionable insights for pedagogical refinement, enabling early identification of at-risk students and targeted interventions that improved outcomes by 23%. The study makes significant contributions to educational technology by demonstrating how data-driven approaches can transform the assessment of complex competency development, while simultaneously advancing the integration of sustainability principles in FinTech education. The findings offer practical guidelines for educators and institutions seeking to enhance their pedagogical practices through evidence-based approaches that align with both educational objectives and global sustainability imperatives.
Copyright (c) 2025 Dr. Sanjay Agal, Ms. Niyati Dhirubhai Odedra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Fifth-generation (5G) frameworks are now at the leading edge of the worldwide digitization due to the quick development of wireless technology for communication. Automated places, automated factories, driverless cars, distant medical services, and engaging activities like virtual and augmented reality are merely some of the broad purposes made possible by 5G's possibilities, which include ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC). Nevertheless, 5G communications unparalleled size, intricacy, and rapid evolution provide serious problems for safety, flexibility, conservation of energy, and productivity improvement. AI improves anomaly identification, invasion mitigation, and continuous surveillance of possible breaches. AI networks provide novel safety vulnerabilities due to their susceptibility to inductive breaches, data compromise, and adversary management. With an emphasis on its use across infrastructure construction, operation efficiency, and privacy, this study offers a thorough examination of the incorporation of AI with 5G networking. It primarily explores the use of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) approaches to enhance traffic identification, simplify system division, enhance the utilization of resources, and facilitate autonomous systems. High Quality of Service (QoS) may be maintained regardless of crowded, adequate-demand settings by employing AI-driven solutions that enable providers to proactively predict and react to changing circumstances affecting the network. It promotes longevity by facilitating adaptive distribution of loads among the network elements and reducing utilization of energy. The abstract also emphasizes AI's hybrid safety function.
Copyright (c) 2026 SONA D SOLANKI, DR. PREM PAL SINGH, DR. KALPESH R JADAV, ASHA D SOLANKI (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Natural Language Processing (NLP) for Multilingual Analysis
This paper explores the application of Natural Language Processing (NLP) techniques for multilingual analysis, addressing the challenges and methodologies involved in processing and analyzing text across diverse languages. It reviews recent advancements in language models, data preprocessing, and system architectures that facilitate effective multilingual understanding. The study proposes a comprehensive framework and evaluates its performance using benchmark datasets, demonstrating improvements in accuracy for multilingual text processing tasks. The results underscore the potential impact of such systems on enhancing global communication, information retrieval, and machine translation services.
Copyright (c) 2026 bhavik tadvi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Depth-Adaptive Routing Mechanisms in Recursive Language Models: A Comprehensive Analysis of Computational Efficiency and Performance Trade-offs
The exponential proliferation of large language models (LLMs) has engendered substantial computational challenges, necessitating innovative methodologies to optimize inference efficiency while preserving performance accuracy. This paper offers a comprehensive analysis of depth-adaptive routing mechanisms in recursive language models, scrutinizing their theoretical foundations, implementation strategies, and comparative performance across a spectrum of architectures. Through a systematic evaluation of Q1-journal studies, we examine how dynamic routing strategies empower models to adaptively allocate computational resources in accordance with input complexity and task exigencies. Our findings elucidate that depth-adaptive routing mechanisms realize an average accuracy enhancement of 17.79% (σ = 13.70) while concurrently diminishing computational overhead by 21.01% (σ = 12.44) across various model architectures. We propose a cohesive mathematical framework for characterizing adaptive routing functions and present empirical evidence illustrating that mixture-of-experts architectures with expert-choice routing surpass traditional token-choice methods, achieving 50% swifter inference speeds with 90% memory efficiency. The theoretical analysis establishes complexity bounds for disparate routing strategies, demonstrating that adaptive token routing attains O(n log n d) time complexity in contrast to O(n²d) for dense transformers. These contributions furnish foundational insights for the development of next-generation efficient language models and establish benchmarks for evaluating adaptive routing mechanisms in production deployments.
Copyright (c) 2025 Nikunj Bhavsar (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
In order to determine the type of evaluation with the attributes utilised, the methods employed, the methods most frequently used, and the methods with the greatest performance, this research was carried out using a literature review method to analyse various studies. Based on pre-established criteria, 40 publications were selected from research conducted between 2016 and July 2021 for this study. Four research topics estimation, classification, clustering, and association—were determined by this review. Four research topics fundamentals, technical, sentiment analysis, and even a combination with analyses that make use of their respective qualities and datasets—are the results of this study. Thirty-one different methods have been identified for stock price prediction. The most popular approaches are LSTM, MLP, RF, and SVM. Furthermore, MLP is a technique that yields the best results, with an LSTM of 70% and a performance of 71.63%. For better precision, it is advised to employ deep learning, ensemble approaches, mixed machine learning, and input attribute selection at pre-processing.
Stock Price Prediction, LSTM, Machine Learning, Time Series Forecasting, Financial Analytics, Plotly Dash.
Copyright (c) 2026 Pranjalee Sharma (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Breast Cancer Prediction using Machine Learning Techniques
Breast cancer is one of the leading causes of death among women worldwide. Early detection is crucial for increasing survival rates. In this study, we evaluate and compare several supervised machine learning algorithms for classifying breast cancer tumors as benign or malignant using the Wisconsin Breast Cancer Dataset. The preprocessing stage involved handling missing values, feature scaling, and skewness removal using mathematical transformations. Feature selection was applied to improve classification performance. Six algorithms—Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine—were implemented in Python (Anaconda, Jupyter Notebook) and evaluated using 10-fold cross-validation. Experimental results demonstrate that Logistic Regression and SVM achieved the highest accuracy (98.24%), surpassing results reported in previous studies. This paper highlights the potential of these techniques for developing robust Computer-Aided Diagnosis systems.
Copyright (c) 2026 Sandip Chakraborty, Aritra Das, Subhadeep Das, Arya Manikya Sinha (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Optimizing Passenger Comfort in Rail Transit via Predictive Jerk Estimation Models
Increasing train acceleration and deceleration can improve system performance in a railway network. However, passengers are also more likely to lose their balance and collapse. The purpose of this article is to investigate the effects of longitudinal vehicle accelerations on passenger comfort and safety. In addition to the results of previous empirical studies on the maximum acceleration that train passengers can tolerate, the literature review combines two different academic fields to investigate the physiological and kinesiological impacts of acceleration on balance [2]. This Research paper proposes a novel machine learning (ML)-based approach for real-time jerk prediction to enhance train passenger stability. We look into a variety of machine learning methods, including deep learning techniques (particularly, recurrent neural networks, or LSNs). and supervised learning models (Random Forest, Support Vector Machines, and Gradient Boosting), [3] to forecast train dynamics using sensor information such as velocity, acceleration, and position. Our method forecasts short jerk incidents using time-series analysis, allowing for real-time passenger alert systems or proactive changes to train operations. We employ rigorous cross-validation to verify the performance of the mentioned algorithms, evaluating F1-score, accuracy, precision, and recall. The results were outstanding and demonstrate that models based on deep learning, particularly LSTM networks, outperform traditional methods by offering higher prediction accuracy in dynamic scenarios.
JERK PREDICTION, LONG SHORT-TERM MEMORY (LSTM), RANDOM FOREST (RF), SUPPORT VECTOR MACHINE (SVM),GRADIENT BOOSTING (GB)
Copyright (c) 2025 Dr Syed Ibad Ali (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Convolutional Neural Network BasedCotton Leaf Disease Classification LeveragingTransfer Learning
Cotton is one of the most important crops in Indian agriculture, serving as the primary raw material for the textile industry. However, its productivity is severely impacted by various leaf diseases, making the early and accurate disease detection crucial for effective crop management and yield improvement. Traditional disease identification methods rely on manual inspection, which is time-consuming, labor-intensive, and prone to errors. To address these challenges, this study proposes an automated cotton leaf disease classification system using deep convolutional neural networks (CNNs) with transfer learning. We evaluate three pretrained CNN models—InceptionV3, ResNet152V2, and MobileNetV2—for image-based disease classification. These models leverage transfer learning to enhance feature extraction and classification accuracy while reducing training time. The system is trained and tested on a benchmark cotton leaf disease dataset, and its performance is assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results demonstrate that transfer learning significantly improves classification performance, enabling rapid and reliable disease identification and empowering farmers with an automated tool to take timely preventive measures. This research contributes to smart agriculture by integrating AI-driven solutions for sustainable farming and improved cotton yield. \keywords{Deep Learning, Convolutional Neural Network, Transfer Learning, Cotton Leaf Disease Classification, Smart Agriculture.
Copyright (c) 2025 Saikat Samanta, Subhayu Ghosh, Debtanu Ghosh, Dr. Nanda Dulal Jana (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Fraud Detection in Blockchain Transactions Using Graph-Based Deep Learning
A thorough analysis of graph-based deep learning techniques for blockchain transaction fraud detection is presented in this paper. Blockchain networks' decentralized and pseudonymous structure creates special difficulties that make conventional fraud detection strategies useless, calling for sophisticated methods that can examine intricate transactional relationships. In comparison to traditional machine learning techniques, we perform a systematic evaluation of graph neural network (GNN) architectures, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), showing their superior ability to identify complex fraud patterns like money laundering, Ponzi schemes, and phishing attacks. Our work demonstrates how these models take advantage of the intrinsic graph structure of blockchain transactions by using network topology, node features, and edge attributes to accurately identify anomalous activity. The report also looks at important issues facing the industry, such as the need for cross-chain fraud detection capabilities, interpretability requirements for regulatory compliance, and scalability constraints for real-time analysis. We demonstrate through experimental validation on real-world blockchain datasets that attention-based GNNs maintain computational efficiency while achieving notable gains in detection accuracy (F1-score of 0.91). Promising research directions are outlined in the paper's conclusion, including the creation of explainable AI frameworks for forensic investigations and the incorporation of temporal graph networks for dynamic fraud pattern recognition. These developments establish graph-based deep learning as a game-changing strategy for protecting blockchain ecosystems from changing financial crimes.
Copyright (c) 2025 Matangi Gandhi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Detailed Review and Implementation of Dense Neural Networks for Handwritten Digit Recognition
Handwritten digit recognition is an important task in computer vision and deep learning research. It serves as a standard for measuring how well classification algorithms perform. Convolutional neural networks (CNNs) lead this area because they excel at capturing spatial relationships, but fully connected dense neural networks can still perform well if they are properly preprocessed and adjusted. This paper offers a detailed review and practical implementation of a dense feedforward neural network for classifying grayscale handwritten digit images from an MNIST-like dataset. We explain the data preprocessing steps, architecture design, training methods, and evaluation metrics. The model, built using TensorFlow Keras, reaches over 97% validation accuracy in 10 epochs. This shows that dense architectures are still a good choice for simple classification tasks. We also compare our results with previous studies and discuss the model's strengths, weaknesses, and possible improvements.
Copyright (c) 2025 shruti patel (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Cybersecurity Challenges in Industrial Control Systems: A Multidisciplinary Review
<span lang="EN-US" style="font-size: 11.0pt; mso-bidi-font-size: 9.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Generally speaking, this study explores the specific cybersecurity problems faced by Industrial Control Systems (ICS). It emphasizes that these problems are multifaceted due to the integration of various technologies and operational frameworks. The research looks into how ICS are critically vulnerable to cyber threats, collecting and then analyzing data on current vulnerabilities, common threat vectors, and also notable incident case studies across different industrial sectors. Key findings generally reveal that the merging of operational tech with info tech actually intensifies security complexities, and this leads to increased vulnerability to cyber-attacks. Within healthcare, the above findings underscore the heightened risks connected with relying on ICS for essential infrastructure, including critical medical devices, and also hospital operational systems, which highlights the need for robust cybersecurity measures. The implications extend beyond the direct ICS scope, suggesting that enhanced cybersecurity strategies, in most cases, can significantly improve healthcare systems' resilience against cyber threats. By fostering interdisciplinary collaboration between cybersecurity experts, engineers, and healthcare professionals, this research lays the foundation for developing comprehensive strategies that can protect critical industrial environments, and thus ultimately contribute to a more secure and efficient healthcare infrastructure
Copyright (c) 2025 Ms. Niyati Dhirubhai Odedra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Technologies Redefining the Future: A Glimpse into the Age of Intelligence
The 21st century is experiencing a transformative wave driven by smart technologies that are reshaping every aspect of human life, from urban infrastructure to personal healthcare. This paper examines how innovations in artificial intelligence (AI), the Internet of Things (IoT), 5G connectivity, automation, and robotics are revolutionising various sectors, including smart cities, healthcare, industry, and everyday living. Smart technologies facilitate efficient urban planning, real-time health monitoring, intelligent automation in industries, and personalised consumer experiences. However, this transformation also presents challenges related to data privacy, technological dependency, and shifts in the employment landscape. The study highlights the importance of ethical integration and inclusive digital growth, concluding that responsible innovation is crucial for sustainable progress in this digital era.
Smart Technologies, Artificial Intelligence (AI), Internet of Things (IoT), Smart Cities, Smart Healthcare, Automation, Robotics, 5G Connectivity, Digital Transformation, Ethical Challenges
Copyright (c) 2025 Dr Bhavna Bajpai Bhavna (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Agriculture plays a crucial role in human lives, with around 70% of the population directly or indirectly involved in it. However, traditional approaches lack tools to identify diseases in crops and increase agricultural output. Early detection of crop diseases is vital as they impact plant growth and pose a significant threat to food security. Machine Learning (ML) models have been used to identify and categorize agricultural diseases, but recent advancements in Deep Learning show promise for improved accuracy. This proposed method utilizes convolutional neural networks and deep neural networks to effectively and accurately identify and recognize crop disease symptoms. By training deep learning models using large publicly available datasets, it becomes possible to detect plant diseases on a large scale.
Plant Disease Detection, ResNet50, Machine learning, Deep Neural Network, Transfer Learning
Copyright (c) 2026 Mohammad Arif, Arun Kumar Marandi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Comparative Study and Design for Construction of An Automatic Sanitizing Floor Cleaning Robot
This research paper proposes the development and deployment of an automated sanitizing robot integrated with a camera. The paper also compares this robot for cleaning floors to a robot for cleaning floors. G power software is used to find the sample size for each group, taking into account predefined parameters and aiming for the best performance with a maximum error of 0.05. This is done to make sure the results are correct. A fixed sample size of 20 is selected for each group for analytical purposes. The design of the automatic controlled sanitizing robot incorporates various components such as Arduino, motor shield, camera, and chassis. The Arduino IDE app is employed to program the Arduino Board. The effectiveness and reliability of the proposed methods are evaluated using a 2-tailed statistical analysis. Additionally, this paper suggests that the sanitizing robot outperforms the floor cleaning robot when it comes to spraying on a flat surface.
Copyright (c) 2025 ARUN KUMAR MARANDI ARUN (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Participation of AI Technologies in Financial Services DEFENSE AGAINST CYBER-ATTACKS
This abstract highlights the role of AI technologies in bolstering the defense against cyber-attacks in financial services. AI empowers organizations to proactively detect and respond to threats by analyzing vast amounts of data. It enables real-time anomaly detection, incident response automation, and fraud prevention. The integration of AI technologies strengthens the security posture of financial institutions and helps mitigate the risks associated with cyber-attacks.
Copyright (c) 2025 Dr. Neelesh Kumar Jain https://creativecommons.org/licenses/by-nc-sa/4.0